The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A Fitting Model for Feature Selection With Fuzzy Rough Sets
Abstract
A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy rough dependency as a criterion for feature selection. However, this model can merely maintain a maximal dependency function. It does not fit a given dataset well and cannot ideally describe the differences in sample classification. Therefore, in this study, we introduce a new model for handling this problem. First, we define the fuzzy decision of a sample using the concept of fuzzy neighborhood. Then, a parameterized fuzzy relation is introduced to characterize the fuzzy information granules, using which the fuzzy lower and upper approximations of a decision are reconstructed and a new fuzzy rough set model is introduced. This can guarantee that the membership degree of a sample to its own category reaches the maximal value. Furthermore, this approach can fit a given dataset and effectively prevents samples from being misclassified. Finally, we define the significance measure of a candidate attribute and design a greedy forward algorithm for feature selection. Twelve datasets selected from public data sources are used to compare the proposed algorithm with certain existing algorithms, and the experimental results show that the proposed reduction algorithm is more effective than classical fuzzy rough sets, especially for those datasets for which different categories exhibit a large degree of overlap.
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Cites
Pawlak, Z. (1982) · International Journal of Computer & Information Sciences · doi:10.1007/bf01001956 · Cited by 12,322
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,544
Multiple comparisons among means
Dunn, O. J. (1961) · Journal of the American Statistical Association · doi:10.1080/01621459.1961.10482090 · Cited by 5,006
Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025 · cited 1×
“Distance [26], [45], correlation [11], consistency [6], and mutual information [25], [37], [48] are usually regarded as being feasible feature evaluation functions.”
BackgroundROUGH FUZZY SETS AND FUZZY ROUGH SETS*
Dubois, D., & Prade, H. M. (1990) · International Journal of General Systems · doi:10.1080/03081079008935107 · Cited by 3,012
A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
Friedman, M. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731944 · Cited by 2,785 · cited 1×
“In addition, we apply the Friedman test [10] and the Bonferroni–Dunn test [8] to show the statistical significance of the results.”
MethodToward integrating feature selection algorithms for classification and clustering
Liu, H., & Yu, L. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.66 · Cited by 2,706
Feature Selection for Knowledge Discovery and Data Mining
Liu, H., & Motoda, H. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5689-3 · Cited by 2,198
Pawlak, Z., & Skowron, A. (2007) · Information Sciences · doi:10.1016/j.ins.2006.06.003 · Cited by 2,070
Metadata
- Authors
- Wang, C., Qi, Y., Shao, M., Hu, Q., Chen, D., Qian, Y., & Lin, Y.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 2017-08
- Volume
- 25
- Issue
- 4
- Pages
- 741-753
- ISSN
- 1063-6706 (ISSN-L)
- 1941-0034
- OpenAlex
- W2416404380
- Cited by
- 266
- References
- 33
- FWCI
- 26.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, C., Qi, Y., Shao, M., Hu, Q., Chen, D., Qian, Y., & Lin, Y. (2017). A Fitting Model for Feature Selection With Fuzzy Rough Sets. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/tfuzz.2016.2574918
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